Though some of the above approaches have achieved relative improvements on some credit scoring datasets [1]}[2]}, they cannot achieve optimal performances due to the lack of consideration of some key factors.
Specifically, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated in real credit scoring applications, according to both real-world data study and theoretical analysis in Sec. .
Intuitively speaking, with an effective credit approval system, rejected customers have higher default ratios, while approved customers have lower ones.
Consequently, the learning of default/non-default can benefit from the learning of rejection/approval.
Accordingly, it might be promising to incorporate Multi-Task Learning (MTL) [3]} for modeling biased credit scoring data.
